Computer Science > Computer Vision and Pattern Recognition
arXiv:2311.13099 (cs)
[Submitted on 22 Nov 2023 (v1), last revised 27 Mar 2024 (this version, v2)]
Title:PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF
View a PDF of the paper titled PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF, by Yutao Feng and 5 other authors
View PDFHTML (experimental)Abstract:We show that physics-based simulations can be seamlessly integrated with NeRF to generate high-quality elastodynamics of real-world objects. Unlike existing methods, we discretize nonlinear hyperelasticity in a meshless way, obviating the necessity for intermediate auxiliary shape proxies like a tetrahedral mesh or voxel grid. A quadratic generalized moving least square (Q-GMLS) is employed to capture nonlinear dynamics and large deformation on the implicit model. Such meshless integration enables versatile simulations of complex and codimensional shapes. We adaptively place the least-square kernels according to the NeRF density field to significantly reduce the complexity of the nonlinear simulation. As a result, physically realistic animations can be conveniently synthesized using our method for a wide range of hyperelastic materials at an interactive rate. For more information, please visit our project page atthis https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2311.13099 [cs.CV] |
| (orarXiv:2311.13099v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2311.13099 arXiv-issued DOI via DataCite |
Submission history
From: Yutao Feng [view email][v1] Wed, 22 Nov 2023 01:58:26 UTC (29,862 KB)
[v2] Wed, 27 Mar 2024 23:49:07 UTC (16,200 KB)
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View a PDF of the paper titled PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF, by Yutao Feng and 5 other authors
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